Stock Market Integration and Trade in Africa: A Gravity Model Approach

This study employs an unbalanced panel dataset covering 50 African countries over the period 2010–2019, yielding a total of 2,003 bilateral observations. The dataset combines bilateral trade data with macroeconomic, structural, and financial indicators to estimate an extended gravity model of trade. Data are compiled from multiple sources to ensure broad coverage and reliability.

Bilateral trade flows, the dependent variable, are measured as the value of exports from country i to country j. Trade data are obtained from the World Integrated Trade Solution (WITS) database and expressed in logarithmic form for descriptive analysis, while the estimation strategy allows for zero trade flows through the use of the Poisson Pseudo Maximum Likelihood (PPML) estimator.

Economic size is captured using gross domestic product (GDP) of both exporting and importing countries, sourced from the World Development Indicators (WDI) of the World Bank. GDP is expressed in logarithmic terms and reflects the productive capacity and market size of trading partners. Population data for exporters and importers, also drawn from WDI, are included to further account for market size and demand effects.

Geographic trade costs are proxied by bilateral distance, measured as the great-circle distance between capital cities. Distance is time-invariant and captures transportation costs, information frictions, and other spatial barriers to trade. Structural conditions affecting trade are represented by an infrastructure index, which reflects the quality of transport and related facilities and varies across countries and time.

Macroeconomic stability is measured using inflation, defined as the annual percentage change in consumer prices. Higher inflation is expected to signal economic instability and increase transaction costs, potentially discouraging trade. Foreign direct investment (FDI) inflows are included as a measure of cross-border capital movements and financial openness, with ambiguous expected effects on trade depending on whether FDI complements or substitutes for goods trade.

The key variable of interest, stock market integration, is proxied by the All Share Index (ASI) for both exporting and importing countries. These indices reflect the overall performance and depth of national stock markets and are used to capture the degree of financial market development and integration between trading partners. Stock market data are obtained from proprietary sources and matched to the bilateral trade dataset.

To capture differences in production structures between countries, the study includes economic endowment differences, defined as the absolute difference in GDP between trading partners. Larger endowment gaps are expected to reduce trade complementarities and weaken bilateral trade flows.

Table 4 presents descriptive statistics for all variables used in the analysis. The data exhibit substantial variation across countries and over time, which is essential for identifying gravity relationships in a panel framework. Notably, bilateral trade flows display significant dispersion and include zero values, justifying the use of PPML estimation. Stock market indices also show wide variation, indicating heterogeneous levels of financial development across African economies.

Overall, the dataset provides a comprehensive representation of trade, financial integration, and macroeconomic conditions in Africa, allowing for a robust empirical assessment of the relationship between stock market integration and bilateral trade within the gravity model framework. This dataset supports an empirical analysis of the relationship between stock market integration and bilateral trade flows among African economies from 2010 to 2019 using a gravity model estimated via Poisson Pseudo Maximum Likelihood (PPML). Data are compiled from the World Bank, WITS, and proprietary stock market sources. All data processing and estimation procedures are fully documented to ensure reproducibility.